Topic: COVID-19 New cases visualisation dashboard: Shiny app
covid = read.csv("owid-covid-data.csv")
# these are indices for each country (an index is just a collection of stocks)
# 'FileEncoding' just cleans the column encoding for this case
#sp500 = read.csv("SPY Historical Data.csv", fileEncoding = 'UTF-8-BOM') # This is for US
#TOPIX = read.csv("TOPIX Historical Data.csv", fileEncoding = 'UTF-8-BOM') # This is for Japan
ASX200 = read.csv("S&P_ASX 200 Historical Data.csv", fileEncoding = 'UTF-8-BOM') # This is for Australia
#NSEI = read.csv("Nifty 50 Historical Data.csv", fileEncoding = 'UTF-8-BOM') # This is for India
#SSEC = read.csv("Shanghai Composite Historical Data.csv", fileEncoding = 'UTF-8-BOM') # this is for China
colnames(covid)## [1] "iso_code"
## [2] "continent"
## [3] "location"
## [4] "date"
## [5] "total_cases"
## [6] "new_cases"
## [7] "new_cases_smoothed"
## [8] "total_deaths"
## [9] "new_deaths"
## [10] "new_deaths_smoothed"
## [11] "total_cases_per_million"
## [12] "new_cases_per_million"
## [13] "new_cases_smoothed_per_million"
## [14] "total_deaths_per_million"
## [15] "new_deaths_per_million"
## [16] "new_deaths_smoothed_per_million"
## [17] "reproduction_rate"
## [18] "icu_patients"
## [19] "icu_patients_per_million"
## [20] "hosp_patients"
## [21] "hosp_patients_per_million"
## [22] "weekly_icu_admissions"
## [23] "weekly_icu_admissions_per_million"
## [24] "weekly_hosp_admissions"
## [25] "weekly_hosp_admissions_per_million"
## [26] "total_tests"
## [27] "new_tests"
## [28] "total_tests_per_thousand"
## [29] "new_tests_per_thousand"
## [30] "new_tests_smoothed"
## [31] "new_tests_smoothed_per_thousand"
## [32] "positive_rate"
## [33] "tests_per_case"
## [34] "tests_units"
## [35] "total_vaccinations"
## [36] "people_vaccinated"
## [37] "people_fully_vaccinated"
## [38] "total_boosters"
## [39] "new_vaccinations"
## [40] "new_vaccinations_smoothed"
## [41] "total_vaccinations_per_hundred"
## [42] "people_vaccinated_per_hundred"
## [43] "people_fully_vaccinated_per_hundred"
## [44] "total_boosters_per_hundred"
## [45] "new_vaccinations_smoothed_per_million"
## [46] "new_people_vaccinated_smoothed"
## [47] "new_people_vaccinated_smoothed_per_hundred"
## [48] "stringency_index"
## [49] "population"
## [50] "population_density"
## [51] "median_age"
## [52] "aged_65_older"
## [53] "aged_70_older"
## [54] "gdp_per_capita"
## [55] "extreme_poverty"
## [56] "cardiovasc_death_rate"
## [57] "diabetes_prevalence"
## [58] "female_smokers"
## [59] "male_smokers"
## [60] "handwashing_facilities"
## [61] "hospital_beds_per_thousand"
## [62] "life_expectancy"
## [63] "human_development_index"
## [64] "excess_mortality_cumulative_absolute"
## [65] "excess_mortality_cumulative"
## [66] "excess_mortality"
## [67] "excess_mortality_cumulative_per_million"
#Dropping NA
covid_clean = covid %>% drop_na(new_cases, new_cases_smoothed, new_vaccinations, new_vaccinations_smoothed, new_vaccinations_smoothed_per_million, population, population_density, median_age, extreme_poverty, total_vaccinations, hospital_beds_per_thousand, human_development_index, new_deaths, new_tests, weekly_icu_admissions_per_million, weekly_icu_admissions)
#This code changes the negative case values in the data set to zero
covid_clean$new_cases[covid_clean$new_cases < 0] <- 0
covid_AUS <- covid %>% filter(location == "Australia")
covid_clean_AUS = covid_AUS %>% drop_na(new_cases, new_cases_smoothed, new_vaccinations, new_vaccinations_smoothed, new_vaccinations_smoothed_per_million, population, population_density, median_age, extreme_poverty, total_vaccinations, hospital_beds_per_thousand, human_development_index, new_deaths, new_tests)
#This code changes the negative case values in the data set to zero
covid_clean_AUS$new_cases[covid_clean_AUS$new_cases < 0] <- 0
view(covid_clean_AUS)
#glimpse(covid_clean)
#covid_clean$date = as.Date(covid_clean$date)
#max(covid_clean$date)
#unique(covid_clean$location)#code for more optimised join (to be pasted later)
class(covid_clean$date)## [1] "character"
# using a copy just in case
covid2 <- covid_clean_AUS
#sp500$Date = mdy(sp500$Date)
#TOPIX$Date = mdy(TOPIX$Date)
ASX200$date = mdy(ASX200$Date)
#NSEI$Date = mdy(NSEI$Date)
#SSEC$Date = mdy(SSEC$Date)
#sp500$date = mdy(sp500$Date)
#TOPIX$date = mdy(TOPIX$Date)
ASX200$date = mdy(ASX200$Date)
#NSEI$date = mdy(NSEI$Date)
#SSEC$date = mdy(SSEC$Date)
covid2$date = ymd(covid2$date)
#Temporarily changing the date to character as joining cannot be done with date objects
#Also selecting relevant columns for analysis later
covid2 <- covid2 %>%
transform(covid2, date = as.character(date)) %>%
select(date, new_cases, new_deaths, location, new_vaccinations, new_tests, population, population_density)
#sp500$Date <- as.character(sp500$Date)
#TOPIX$Date <- as.character(TOPIX$Date)
ASX200$date <- as.character(ASX200$date)
#NSEI$Date <- as.character(NSEI$Date)
#SSEC$Date <- as.character(SSEC$Date)
#sp500$date <- as.character(sp500$Date)
#TOPIX$date <- as.character(TOPIX$Date)
ASX200$date <- as.character(ASX200$date)
#NSEI$date <- as.character(NSEI$Date)
#SSEC$date <- as.character(SSEC$Date)
# renaming column so it has same name as the stock market data frames for joining later
#colnames(covid2)[1] = "Date"
# making data frames for each country we select to perform individual joins on each to their respective stock market index
#covid_US <- covid2 %>% filter(location == "United States")
covid_AUS <- covid2 %>% filter(location == "Australia")
#covid_IND <- covid2 %>% filter(location == "India")
#covid_JPN <- covid2 %>% filter(location == "Japan")
#covid_CHN <- covid2 %>% filter(location == "China")
# performing joins
#df_1 = inner_join(sp500, covid_US, by = "Date")
#df_2 = inner_join(TOPIX, covid_JPN, by = "Date")
df_3 = inner_join(ASX200, covid_AUS, by = "date")
#df_4 = inner_join(NSEI, covid_IND, by = "Date")
#df_5 = inner_join(SSEC, covid_CHN, by = "Date")
#df_1 = inner_join(sp500, covid_US, by = "date")
#df_2 = inner_join(TOPIX, covid_JPN, by = "date")
df_3 = inner_join(ASX200, covid_AUS, by = "date")
#df_4 = inner_join(NSEI, covid_IND, by = "date")
#df_5 = inner_join(SSEC, covid_CHN, by = "date")
# vertically joined data set (now one column will store all the values of the respective country index)
# e.g US stores prices relevant to S&P500 and China's prices are relevant to the the SSEC which is based in Shanghai.
#covid_joined <- rbind(df_1, df_2, df_3, df_4, df_5)
# Still need transform relevant column to numeric ect...will do a little later
df_aus <- df_3
#transformation
df_aus$date = as.Date(df_aus$date)
df_aus$Price = as.numeric(gsub(",","",df_aus$Price))
df_aus## Date Price Open High Low Vol. Change.. date
## 1 Mar 25, 2022 7406.2 7,387.10 7,431.30 7,387.10 679.65M 0.26% 2022-03-25
## 2 Mar 24, 2022 7387.1 7,377.90 7,399.40 7,356.20 633.60M 0.12% 2022-03-24
## 3 Mar 23, 2022 7377.9 7,341.10 7,386.90 7,329.80 575.42M 0.50% 2022-03-23
## 4 Mar 22, 2022 7341.1 7,278.50 7,377.30 7,278.50 634.67M 0.86% 2022-03-22
## 5 Mar 21, 2022 7278.5 7,294.40 7,350.40 7,278.50 561.98M -0.22% 2022-03-21
## 6 Mar 18, 2022 7294.4 7,250.80 7,294.40 7,245.90 1.54B 0.60% 2022-03-18
## 7 Mar 17, 2022 7250.8 7,175.20 7,296.80 7,175.20 831.38M 1.05% 2022-03-17
## 8 Mar 16, 2022 7175.2 7,097.40 7,180.20 7,097.40 701.73M 1.10% 2022-03-16
## 9 Mar 15, 2022 7097.4 7,149.40 7,149.40 7,080.70 788.37M -0.73% 2022-03-15
## 10 Mar 14, 2022 7149.4 7,063.60 7,149.40 7,063.60 599.30M 1.21% 2022-03-14
## 11 Mar 11, 2022 7063.6 7,130.80 7,151.40 7,051.20 816.85M -0.94% 2022-03-11
## 12 Mar 10, 2022 7130.8 7,053.00 7,161.00 7,039.70 976.57M 1.10% 2022-03-10
## 13 Mar 09, 2022 7053.0 6,980.30 7,072.00 6,968.90 1.04B 1.04% 2022-03-09
## 14 Mar 08, 2022 6980.3 7,038.60 7,053.50 6,980.30 973.85M -0.83% 2022-03-08
## 15 Mar 07, 2022 7038.6 7,110.80 7,133.70 7,010.50 1.02B -1.02% 2022-03-07
## 16 Mar 04, 2022 7110.8 7,151.40 7,151.40 7,025.20 972.29M -0.57% 2022-03-04
## 17 Mar 03, 2022 7151.4 7,116.70 7,198.10 7,116.70 856.24M 0.49% 2022-03-03
## 18 Mar 02, 2022 7116.7 7,096.50 7,121.50 7,041.90 941.03M 0.28% 2022-03-02
## 19 Mar 01, 2022 7096.5 7,049.10 7,159.90 7,049.10 812.87M 0.67% 2022-03-01
## 20 Feb 28, 2022 7049.1 6,997.80 7,049.60 6,979.40 1.01B 0.73% 2022-02-28
## 21 Feb 25, 2022 6997.8 6,990.60 7,045.60 6,974.90 888.81M 0.10% 2022-02-25
## 22 Feb 24, 2022 6990.6 7,205.70 7,205.70 6,959.30 1.11B -2.99% 2022-02-24
## 23 Feb 23, 2022 7205.7 7,161.30 7,205.70 7,145.40 788.38M 0.62% 2022-02-23
## 24 Feb 15, 2022 7206.9 7,243.90 7,251.40 7,198.00 730.13M -0.51% 2022-02-15
## 25 Feb 14, 2022 7243.9 7,217.30 7,263.60 7,181.70 749.35M 0.37% 2022-02-14
## 26 Feb 11, 2022 7217.3 7,288.50 7,294.40 7,191.80 629.39M -0.98% 2022-02-11
## 27 Feb 10, 2022 7288.5 7,268.30 7,336.60 7,260.80 886.64M 0.28% 2022-02-10
## 28 Feb 09, 2022 7268.3 7,186.70 7,268.30 7,183.00 978.75M 1.14% 2022-02-09
## 29 Feb 08, 2022 7186.7 7,110.80 7,203.00 7,110.80 771.03M 1.07% 2022-02-08
## 30 Feb 07, 2022 7110.8 7,120.20 7,128.30 7,046.50 617.94M -0.13% 2022-02-07
## 31 Jan 28, 2022 6988.1 6,838.30 7,000.00 6,837.90 2.09B 2.19% 2022-01-28
## 32 Jan 27, 2022 6838.3 6,961.60 7,042.80 6,758.20 1.18B -1.77% 2022-01-27
## 33 Jan 25, 2022 6961.6 7,139.50 7,139.50 6,920.70 1.10B -2.49% 2022-01-25
## 34 Jan 24, 2022 7139.5 7,173.70 7,173.70 7,086.80 760.46M -0.51% 2022-01-24
## 35 Jan 21, 2022 7175.8 7,342.40 7,342.40 7,153.30 910.99M -2.27% 2022-01-21
## 36 Jan 20, 2022 7342.4 7,332.50 7,354.60 7,298.50 684.95M 0.14% 2022-01-20
## 37 Jan 19, 2022 7332.5 7,408.80 7,408.80 7,325.70 697.49M -1.03% 2022-01-19
## 38 Jan 18, 2022 7408.8 7,417.30 7,445.10 7,398.20 549.27M -0.11% 2022-01-18
## 39 Jan 17, 2022 7417.3 7,393.90 7,429.30 7,385.80 472.82M 0.32% 2022-01-17
## 40 Jan 14, 2022 7393.9 7,474.40 7,474.40 7,386.80 619.98M -1.08% 2022-01-14
## 41 Jan 13, 2022 7474.4 7,438.90 7,487.30 7,438.90 605.24M 0.48% 2022-01-13
## 42 Jan 12, 2022 7438.9 7,390.10 7,467.50 7,390.10 569.40M 0.66% 2022-01-12
## 43 Jan 11, 2022 7390.1 7,447.10 7,447.10 7,376.80 549.65M -0.77% 2022-01-11
## 44 Jan 10, 2022 7447.1 7,453.30 7,460.20 7,409.90 410.07M -0.08% 2022-01-10
## 45 Jan 07, 2022 7453.3 7,358.30 7,484.60 7,358.30 469.08M 1.29% 2022-01-07
## 46 Jan 06, 2022 7358.3 7,565.80 7,565.80 7,340.40 623.92M -2.74% 2022-01-06
## 47 Jan 05, 2022 7565.8 7,589.80 7,620.20 7,563.40 472.27M -0.32% 2022-01-05
## 48 Jan 04, 2022 7589.8 7,444.60 7,594.70 7,444.60 503.96M 1.95% 2022-01-04
## 49 Dec 31, 2021 7444.6 7,513.40 7,515.30 7,444.60 272.41M -0.92% 2021-12-31
## 50 Dec 30, 2021 7513.4 7,509.80 7,520.60 7,499.50 365.35M 0.05% 2021-12-30
## 51 Dec 29, 2021 7509.8 7,420.30 7,518.40 7,420.30 428.61M 1.21% 2021-12-29
## 52 Dec 24, 2021 7420.3 7,387.60 7,435.90 7,387.60 230.00M 0.44% 2021-12-24
## 53 Dec 23, 2021 7387.6 7,364.80 7,404.20 7,363.60 390.03M 0.31% 2021-12-23
## 54 Dec 22, 2021 7364.8 7,355.00 7,374.50 7,333.00 539.85M 0.13% 2021-12-22
## 55 Dec 21, 2021 7355.0 7,292.20 7,358.30 7,287.30 571.41M 0.86% 2021-12-21
## 56 Dec 20, 2021 7292.2 7,304.00 7,304.00 7,257.80 543.60M -0.16% 2021-12-20
## 57 Dec 17, 2021 7304.0 7,295.70 7,350.30 7,295.70 1.22B 0.11% 2021-12-17
## 58 Dec 16, 2021 7295.7 7,327.10 7,334.50 7,277.10 787.43M -0.43% 2021-12-16
## 59 Dec 15, 2021 7327.1 7,378.40 7,378.40 7,325.70 514.72M -0.70% 2021-12-15
## 60 Dec 14, 2021 7378.4 7,379.30 7,393.50 7,341.30 581.39M -0.01% 2021-12-14
## 61 Dec 13, 2021 7379.3 7,353.50 7,417.70 7,353.50 452.15M 0.35% 2021-12-13
## 62 Dec 10, 2021 7353.5 7,384.50 7,384.50 7,336.70 599.53M -0.42% 2021-12-10
## 63 Dec 09, 2021 7384.5 7,405.40 7,418.30 7,377.70 542.21M -0.28% 2021-12-09
## 64 Dec 08, 2021 7405.4 7,313.90 7,443.40 7,313.90 675.57M 1.25% 2021-12-08
## 65 Dec 07, 2021 7313.9 7,245.10 7,326.10 7,245.10 601.67M 0.95% 2021-12-07
## 66 Dec 06, 2021 7245.1 7,241.20 7,257.90 7,207.80 650.43M 0.05% 2021-12-06
## 67 Dec 03, 2021 7241.2 7,225.20 7,288.20 7,211.50 631.36M 0.22% 2021-12-03
## 68 Dec 02, 2021 7225.2 7,235.90 7,239.70 7,168.90 652.44M -0.15% 2021-12-02
## 69 Dec 01, 2021 7235.9 7,256.00 7,262.30 7,183.40 654.05M -0.28% 2021-12-01
## 70 Nov 30, 2021 7256.0 7,239.80 7,332.60 7,239.80 1.12B 0.22% 2021-11-30
## 71 Nov 29, 2021 7239.8 7,279.30 7,279.30 7,180.30 817.95M -0.54% 2021-11-29
## 72 Nov 26, 2021 7279.3 7,407.30 7,407.30 7,260.30 577.17M -1.73% 2021-11-26
## 73 Nov 25, 2021 7407.3 7,399.40 7,412.70 7,373.70 597.77M 0.11% 2021-11-25
## 74 Nov 24, 2021 7399.4 7,410.60 7,425.00 7,381.80 595.33M -0.15% 2021-11-24
## 75 Nov 23, 2021 7410.6 7,353.10 7,416.60 7,353.10 606.30M 0.78% 2021-11-23
## 76 Nov 22, 2021 7353.1 7,396.50 7,396.50 7,337.00 486.26M -0.59% 2021-11-22
## 77 Nov 19, 2021 7396.5 7,379.20 7,404.90 7,376.00 525.25M 0.23% 2021-11-19
## 78 Nov 18, 2021 7379.2 7,369.90 7,398.20 7,343.80 634.51M 0.13% 2021-11-18
## 79 Nov 17, 2021 7369.9 7,420.40 7,431.10 7,342.90 626.09M -0.68% 2021-11-17
## 80 Nov 16, 2021 7420.4 7,470.10 7,470.10 7,403.50 626.13M -0.67% 2021-11-16
## 81 Nov 15, 2021 7470.1 7,443.00 7,479.10 7,437.90 499.12M 0.36% 2021-11-15
## 82 Nov 12, 2021 7443.0 7,381.90 7,465.80 7,381.90 572.67M 0.83% 2021-11-12
## 83 Nov 11, 2021 7381.9 7,423.90 7,423.90 7,329.50 601.08M -0.57% 2021-11-11
## 84 Nov 10, 2021 7423.9 7,434.20 7,460.50 7,411.90 549.06M -0.14% 2021-11-10
## 85 Nov 09, 2021 7434.2 7,452.20 7,468.60 7,434.20 609.74M -0.24% 2021-11-09
## 86 Nov 08, 2021 7452.2 7,456.90 7,474.30 7,433.40 581.05M -0.06% 2021-11-08
## 87 Nov 05, 2021 7456.9 7,428.00 7,477.20 7,424.40 568.02M 0.39% 2021-11-05
## 88 Nov 04, 2021 7428.0 7,392.70 7,428.00 7,392.70 658.11M 0.48% 2021-11-04
## 89 Nov 03, 2021 7392.7 7,324.30 7,431.60 7,324.30 587.77M 0.93% 2021-11-03
## 90 Nov 02, 2021 7324.3 7,370.80 7,395.90 7,311.60 491.50M -0.63% 2021-11-02
## 91 Nov 01, 2021 7370.8 7,323.70 7,388.30 7,323.70 612.05M 0.64% 2021-11-01
## 92 Oct 29, 2021 7323.7 7,430.40 7,447.00 7,318.80 695.89M -1.44% 2021-10-29
## 93 Oct 28, 2021 7430.4 7,448.70 7,448.70 7,404.60 578.21M -0.25% 2021-10-28
## 94 Oct 27, 2021 7448.7 7,443.40 7,473.90 7,419.70 596.69M 0.07% 2021-10-27
## 95 Oct 26, 2021 7443.4 7,441.00 7,471.30 7,439.20 517.77M 0.03% 2021-10-26
## 96 Oct 25, 2021 7441.0 7,415.50 7,471.70 7,415.50 464.11M 0.34% 2021-10-25
## 97 Oct 22, 2021 7415.5 7,415.40 7,431.40 7,391.30 638.59M 0.00% 2021-10-22
## 98 Oct 21, 2021 7415.4 7,413.70 7,446.70 7,403.70 751.88M 0.02% 2021-10-21
## 99 Oct 20, 2021 7413.7 7,374.90 7,449.50 7,374.90 746.76M 0.53% 2021-10-20
## 100 Oct 19, 2021 7374.9 7,381.10 7,406.90 7,373.80 581.73M -0.08% 2021-10-19
## 101 Oct 18, 2021 7381.1 7,362.00 7,393.80 7,354.10 562.54M 0.26% 2021-10-18
## 102 Oct 15, 2021 7362.0 7,311.70 7,373.20 7,311.70 565.80M 0.69% 2021-10-15
## 103 Oct 14, 2021 7311.7 7,272.50 7,358.60 7,272.50 648.07M 0.54% 2021-10-14
## 104 Oct 13, 2021 7272.5 7,280.70 7,294.00 7,256.30 589.78M -0.11% 2021-10-13
## 105 Oct 12, 2021 7280.7 7,299.80 7,330.40 7,260.40 669.14M -0.26% 2021-10-12
## 106 Oct 11, 2021 7299.8 7,320.10 7,320.10 7,250.00 613.01M -0.28% 2021-10-11
## 107 Oct 08, 2021 7320.1 7,256.70 7,322.00 7,256.70 630.30M 0.87% 2021-10-08
## 108 Oct 07, 2021 7256.7 7,206.50 7,265.20 7,206.50 718.86M 0.70% 2021-10-07
## 109 Oct 06, 2021 7206.5 7,248.40 7,279.40 7,183.50 726.94M -0.58% 2021-10-06
## 110 Oct 05, 2021 7248.4 7,278.50 7,278.50 7,202.70 735.94M -0.41% 2021-10-05
## 111 Oct 04, 2021 7278.5 7,185.50 7,305.90 7,185.50 464.15M 1.29% 2021-10-04
## 112 Oct 01, 2021 7185.5 7,332.20 7,332.20 7,157.90 785.10M -2.00% 2021-10-01
## 113 Sep 30, 2021 7332.2 7,196.70 7,332.20 7,196.70 801.81M 1.88% 2021-09-30
## 114 Sep 29, 2021 7196.7 7,275.60 7,275.60 7,145.70 850.92M -1.08% 2021-09-29
## 115 Sep 28, 2021 7275.6 7,384.20 7,384.20 7,275.60 753.73M -1.47% 2021-09-28
## 116 Sep 27, 2021 7384.2 7,342.60 7,416.40 7,342.60 630.60M 0.57% 2021-09-27
## 117 Sep 24, 2021 7342.6 7,370.20 7,377.00 7,334.20 666.26M -0.37% 2021-09-24
## 118 Sep 23, 2021 7370.2 7,296.90 7,387.00 7,296.90 696.69M 1.00% 2021-09-23
## 119 Sep 22, 2021 7296.9 7,273.80 7,338.60 7,241.80 663.95M 0.32% 2021-09-22
## 120 Sep 21, 2021 7273.8 7,248.20 7,285.90 7,191.70 740.27M 0.35% 2021-09-21
## 121 Sep 20, 2021 7248.2 7,390.40 7,390.40 7,233.60 721.06M -2.10% 2021-09-20
## 122 Sep 17, 2021 7403.7 7,460.20 7,460.20 7,376.10 1.61B -0.76% 2021-09-17
## 123 Sep 16, 2021 7460.2 7,417.00 7,487.60 7,417.00 908.93M 0.58% 2021-09-16
## 124 Sep 15, 2021 7417.0 7,437.30 7,437.30 7,378.90 783.02M -0.27% 2021-09-15
## 125 Sep 14, 2021 7437.3 7,425.20 7,446.00 7,388.90 810.67M 0.16% 2021-09-14
## 126 Sep 13, 2021 7425.2 7,406.60 7,436.80 7,389.40 591.65M 0.25% 2021-09-13
## 127 Sep 10, 2021 7406.6 7,369.50 7,430.00 7,369.50 599.12M 0.50% 2021-09-10
## 128 Sep 09, 2021 7369.5 7,512.00 7,512.00 7,344.30 740.25M -1.90% 2021-09-09
## 129 Sep 08, 2021 7512.0 7,530.30 7,530.30 7,476.30 688.88M -0.24% 2021-09-08
## 130 Sep 07, 2021 7530.3 7,528.50 7,536.90 7,487.80 591.48M 0.02% 2021-09-07
## 131 Sep 06, 2021 7528.5 7,522.90 7,528.50 7,440.10 807.64M 0.07% 2021-09-06
## 132 Sep 03, 2021 7522.9 7,485.70 7,539.00 7,485.70 586.72M 0.50% 2021-09-03
## 133 Sep 02, 2021 7485.7 7,527.10 7,527.10 7,437.20 631.41M -0.55% 2021-09-02
## 134 Sep 01, 2021 7527.1 7,534.90 7,534.90 7,462.20 618.48M -0.10% 2021-09-01
## 135 Aug 31, 2021 7534.9 7,504.50 7,555.90 7,504.50 806.31M 0.41% 2021-08-31
## 136 Aug 30, 2021 7504.5 7,488.30 7,528.30 7,476.00 648.07M 0.22% 2021-08-30
## 137 Aug 27, 2021 7488.3 7,491.20 7,494.50 7,465.00 756.50M -0.04% 2021-08-27
## 138 Aug 26, 2021 7491.2 7,531.90 7,531.90 7,477.80 771.81M -0.54% 2021-08-26
## 139 Aug 25, 2021 7531.9 7,503.00 7,542.40 7,503.00 710.52M 0.39% 2021-08-25
## 140 Aug 24, 2021 7503.0 7,489.90 7,524.40 7,489.90 685.47M 0.17% 2021-08-24
## 141 Aug 23, 2021 7489.9 7,460.90 7,496.30 7,460.90 622.95M 0.39% 2021-08-23
## 142 Aug 20, 2021 7460.9 7,464.60 7,512.00 7,453.60 712.73M -0.05% 2021-08-20
## 143 Aug 19, 2021 7464.6 7,502.10 7,502.10 7,429.20 747.63M -0.50% 2021-08-19
## 144 Aug 18, 2021 7502.1 7,511.00 7,532.90 7,470.50 653.54M -0.12% 2021-08-18
## 145 Aug 17, 2021 7511.0 7,582.50 7,582.50 7,494.40 559.15M -0.94% 2021-08-17
## 146 Aug 16, 2021 7582.5 7,628.90 7,629.00 7,582.50 647.00M -0.61% 2021-08-16
## 147 Aug 13, 2021 7628.9 7,588.20 7,632.80 7,585.90 605.05M 0.54% 2021-08-13
## 148 Aug 12, 2021 7588.2 7,584.30 7,608.60 7,571.50 627.84M 0.05% 2021-08-12
## 149 Aug 11, 2021 7584.3 7,562.60 7,615.10 7,562.60 643.15M 0.29% 2021-08-11
## 150 Aug 10, 2021 7562.6 7,538.40 7,576.30 7,537.10 525.15M 0.32% 2021-08-10
## 151 Aug 09, 2021 7538.4 7,538.40 7,567.00 7,532.80 526.82M 0.00% 2021-08-09
## 152 Aug 06, 2021 7538.4 7,511.10 7,538.40 7,497.30 539.38M 0.36% 2021-08-06
## 153 Aug 05, 2021 7511.1 7,503.20 7,526.40 7,493.00 510.28M 0.11% 2021-08-05
## 154 Aug 04, 2021 7503.2 7,474.50 7,509.20 7,474.50 499.67M 0.38% 2021-08-04
## 155 Aug 03, 2021 7474.5 7,491.40 7,495.90 7,455.50 623.33M -0.23% 2021-08-03
## 156 Aug 02, 2021 7491.4 7,415.90 7,506.30 7,414.90 576.29M 1.34% 2021-08-02
## 157 Jul 30, 2021 7392.6 7,417.40 7,437.70 7,387.90 632.53M -0.33% 2021-07-30
## 158 Jul 29, 2021 7417.4 7,379.30 7,418.70 7,379.30 514.88M 0.52% 2021-07-29
## 159 Jul 28, 2021 7379.3 7,431.40 7,432.40 7,368.30 585.95M -0.70% 2021-07-28
## 160 Jul 26, 2021 7394.3 7,394.40 7,417.60 7,389.30 548.09M -0.00% 2021-07-26
## 161 Jul 23, 2021 7394.4 7,386.40 7,398.70 7,357.00 493.58M 0.11% 2021-07-23
## 162 Jul 22, 2021 7386.4 7,308.70 7,386.40 7,308.70 582.73M 1.06% 2021-07-22
## 163 Jul 21, 2021 7308.7 7,252.20 7,354.80 7,252.20 581.30M 0.78% 2021-07-21
## 164 Jul 20, 2021 7252.2 7,286.00 7,286.00 7,205.00 644.64M -0.46% 2021-07-20
## 165 Jul 19, 2021 7286.0 7,348.10 7,348.10 7,249.20 496.36M -0.85% 2021-07-19
## 166 Jul 16, 2021 7348.1 7,335.90 7,348.10 7,316.30 592.38M 0.17% 2021-07-16
## 167 Jul 15, 2021 7335.9 7,354.70 7,365.70 7,322.90 643.69M -0.26% 2021-07-15
## 168 Jul 14, 2021 7354.7 7,332.10 7,368.50 7,326.50 561.31M 0.31% 2021-07-14
## 169 Jul 13, 2021 7332.1 7,333.50 7,382.20 7,332.10 616.75M -0.02% 2021-07-13
## 170 Jul 12, 2021 7333.5 7,273.30 7,353.40 7,273.30 471.35M 0.83% 2021-07-12
## 171 Jul 09, 2021 7273.3 7,341.40 7,341.40 7,226.00 621.91M -0.93% 2021-07-09
## 172 Jul 08, 2021 7341.4 7,326.90 7,381.90 7,326.90 573.96M 0.20% 2021-07-08
## 173 Jul 07, 2021 7326.9 7,261.80 7,332.40 7,255.50 706.86M 0.90% 2021-07-07
## 174 Jul 06, 2021 7261.8 7,315.00 7,346.20 7,261.80 552.71M -0.73% 2021-07-06
## 175 Jul 05, 2021 7315.0 7,308.60 7,343.70 7,306.70 548.27M 0.09% 2021-07-05
## 176 Jul 02, 2021 7308.6 7,265.60 7,312.30 7,265.60 501.02M 0.59% 2021-07-02
## 177 Jul 01, 2021 7265.6 7,313.00 7,317.20 7,265.60 573.31M -0.65% 2021-07-01
## 178 Jun 30, 2021 7313.0 7,301.20 7,370.10 7,301.20 675.23M 0.16% 2021-06-30
## 179 Jun 29, 2021 7301.2 7,308.00 7,308.00 7,241.50 514.75M -0.08% 2021-06-29
## 180 Jun 28, 2021 7307.3 7,308.00 7,310.60 7,273.70 515.55M -0.01% 2021-06-28
## 181 Jun 25, 2021 7308.0 7,275.30 7,325.60 7,275.30 589.93M 0.45% 2021-06-25
## 182 Jun 24, 2021 7275.3 7,298.50 7,303.80 7,256.30 583.50M -0.32% 2021-06-24
## 183 Jun 23, 2021 7298.5 7,342.20 7,344.00 7,292.90 587.63M -0.60% 2021-06-23
## 184 Jun 22, 2021 7342.2 7,235.30 7,365.90 7,235.30 761.07M 1.48% 2021-06-22
## 185 Jun 21, 2021 7235.3 7,368.90 7,368.90 7,216.60 607.37M -1.81% 2021-06-21
## 186 Jun 18, 2021 7368.9 7,359.00 7,403.20 7,334.90 1.23B 0.13% 2021-06-18
## 187 Jun 17, 2021 7359.0 7,386.20 7,386.20 7,341.90 772.07M -0.37% 2021-06-17
## 188 Jun 16, 2021 7386.2 7,379.50 7,406.20 7,372.40 663.03M 0.09% 2021-06-16
## 189 Jun 15, 2021 7379.5 7,312.30 7,398.60 7,312.30 613.74M 0.92% 2021-06-15
## 190 Jun 10, 2021 7302.5 7,270.20 7,314.60 7,265.60 630.69M 0.44% 2021-06-10
## 191 Jun 09, 2021 7270.2 7,292.60 7,334.90 7,270.20 567.09M -0.31% 2021-06-09
## 192 Jun 08, 2021 7292.6 7,281.90 7,315.60 7,267.60 504.73M 0.15% 2021-06-08
## 193 Jun 07, 2021 7281.9 7,295.40 7,309.40 7,269.40 471.21M -0.19% 2021-06-07
## 194 Jun 04, 2021 7295.4 7,260.10 7,300.50 7,244.30 645.70M 0.49% 2021-06-04
## 195 Jun 03, 2021 7260.1 7,217.80 7,281.80 7,217.80 592.84M 0.59% 2021-06-03
## 196 Jun 02, 2021 7217.8 7,142.60 7,218.90 7,142.60 574.83M 1.05% 2021-06-02
## 197 Jun 01, 2021 7142.6 7,161.60 7,163.00 7,117.50 414.77M -0.27% 2021-06-01
## 198 May 31, 2021 7161.6 7,179.50 7,203.30 7,157.20 499.77M -0.25% 2021-05-31
## 199 May 28, 2021 7179.5 7,094.90 7,186.80 7,094.90 606.89M 1.19% 2021-05-28
## 200 May 27, 2021 7094.9 7,092.50 7,118.90 7,082.40 1.40B 0.03% 2021-05-27
## 201 May 26, 2021 7092.5 7,115.20 7,136.40 7,090.10 517.99M -0.32% 2021-05-26
## 202 May 25, 2021 7115.2 7,045.90 7,115.20 7,045.90 483.42M 0.98% 2021-05-25
## 203 May 20, 2021 7019.6 6,931.70 7,025.40 6,919.40 664.42M 1.27% 2021-05-20
## 204 May 19, 2021 6931.7 7,066.00 7,066.00 6,917.30 702.25M -1.90% 2021-05-19
## 205 May 18, 2021 7066.0 7,023.60 7,083.60 7,023.60 524.94M 0.60% 2021-05-18
## 206 May 17, 2021 7023.6 7,014.20 7,065.70 7,014.20 453.25M 0.13% 2021-05-17
## 207 May 14, 2021 7014.2 6,982.70 7,055.70 6,982.70 520.96M 0.45% 2021-05-14
## 208 May 13, 2021 6982.7 7,044.90 7,044.90 6,966.50 617.00M -0.88% 2021-05-13
## 209 May 12, 2021 7044.9 7,097.00 7,097.00 7,006.60 644.65M -0.73% 2021-05-12
## 210 May 11, 2021 7097.0 7,172.80 7,172.80 7,078.00 558.92M -1.06% 2021-05-11
## 211 May 10, 2021 7172.8 7,080.80 7,172.80 7,076.10 588.42M 1.30% 2021-05-10
## 212 May 07, 2021 7080.8 7,061.70 7,101.20 7,054.10 532.25M 0.27% 2021-05-07
## 213 May 06, 2021 7061.7 7,095.80 7,112.50 7,042.70 625.15M -0.48% 2021-05-06
## 214 May 05, 2021 7095.8 7,067.90 7,122.00 7,054.00 510.16M 0.39% 2021-05-05
## 215 May 04, 2021 7067.9 7,028.80 7,069.50 7,028.80 484.50M 0.56% 2021-05-04
## 216 May 03, 2021 7028.8 7,025.80 7,068.40 7,022.00 468.23M 0.04% 2021-05-03
## 217 Apr 30, 2021 7025.8 7,082.30 7,082.30 7,012.80 615.51M -0.80% 2021-04-30
## 218 Apr 29, 2021 7082.3 7,064.70 7,096.90 7,064.70 634.86M 0.25% 2021-04-29
## 219 Apr 28, 2021 7064.7 7,033.80 7,077.50 7,028.60 620.72M 0.44% 2021-04-28
## 220 Apr 27, 2021 7033.8 7,045.60 7,053.30 7,005.90 596.35M -0.17% 2021-04-27
## 221 Apr 26, 2021 7045.6 7,060.70 7,075.20 7,045.60 427.51M -0.21% 2021-04-26
## 222 Apr 23, 2021 7060.7 7,055.40 7,060.70 7,036.80 485.41M 0.08% 2021-04-23
## 223 Apr 22, 2021 7055.4 6,997.50 7,055.40 6,993.70 663.98M 0.83% 2021-04-22
## 224 Apr 21, 2021 6997.5 7,017.80 7,017.80 6,905.40 689.08M -0.29% 2021-04-21
## 225 Apr 20, 2021 7017.8 7,065.60 7,067.20 7,008.80 554.64M -0.68% 2021-04-20
## 226 Apr 19, 2021 7065.6 7,063.50 7,094.80 7,063.50 473.52M 0.03% 2021-04-19
## 227 Apr 16, 2021 7063.5 7,058.60 7,066.40 7,030.10 627.01M 0.07% 2021-04-16
## 228 Apr 15, 2021 7058.6 7,023.10 7,071.50 6,988.60 686.79M 0.51% 2021-04-15
## 229 Apr 14, 2021 7023.1 6,976.90 7,027.40 6,976.90 576.45M 0.66% 2021-04-14
## 230 Apr 13, 2021 6976.9 6,974.00 6,998.00 6,962.90 507.31M 0.04% 2021-04-13
## 231 Apr 12, 2021 6974.0 6,996.70 6,996.70 6,957.40 405.10M -0.30% 2021-04-12
## 232 Apr 09, 2021 6995.2 6,998.80 6,998.80 6,965.50 425.91M -0.05% 2021-04-09
## 233 Apr 08, 2021 6998.8 6,928.00 7,012.40 6,928.00 544.99M 1.02% 2021-04-08
## 234 Apr 07, 2021 6928.0 6,885.90 6,933.90 6,885.90 612.67M 0.61% 2021-04-07
## 235 Apr 06, 2021 6885.9 6,828.70 6,915.70 6,828.70 517.51M 0.84% 2021-04-06
## 236 Apr 01, 2021 6828.7 6,790.70 6,830.40 6,783.30 473.19M 0.56% 2021-04-01
## 237 Mar 31, 2021 6790.7 6,738.40 6,862.60 6,738.40 717.60M 0.78% 2021-03-31
## 238 Mar 30, 2021 6738.4 6,799.50 6,836.30 6,738.40 513.84M -0.90% 2021-03-30
## 239 Mar 29, 2021 6799.5 6,824.20 6,860.60 6,794.10 502.07M -0.36% 2021-03-29
## 240 Mar 26, 2021 6824.2 6,790.60 6,835.00 6,790.60 571.46M 0.49% 2021-03-26
## 241 Mar 25, 2021 6790.6 6,778.80 6,806.00 6,769.90 558.91M 0.17% 2021-03-25
## 242 Mar 24, 2021 6778.8 6,745.40 6,799.10 6,735.60 503.00M 0.50% 2021-03-24
## 243 Mar 23, 2021 6745.4 6,752.50 6,786.20 6,741.30 518.77M -0.11% 2021-03-23
## 244 Mar 22, 2021 6752.5 6,708.20 6,763.40 6,688.20 472.04M 0.66% 2021-03-22
## 245 Mar 19, 2021 6708.2 6,745.90 6,745.90 6,673.70 1.25B -0.56% 2021-03-19
## 246 Mar 18, 2021 6745.9 6,795.20 6,806.20 6,744.50 715.60M -0.73% 2021-03-18
## 247 Mar 17, 2021 6795.2 6,827.10 6,827.10 6,761.40 603.82M -0.47% 2021-03-17
## 248 Mar 16, 2021 6827.1 6,773.00 6,858.90 6,767.80 587.15M 0.80% 2021-03-16
## 249 Mar 15, 2021 6773.0 6,766.80 6,793.50 6,727.50 484.55M 0.09% 2021-03-15
## 250 Mar 12, 2021 6766.8 6,713.90 6,783.00 6,713.90 468.30M 0.79% 2021-03-12
## 251 Mar 11, 2021 6713.9 6,714.10 6,756.70 6,648.60 665.89M -0.00% 2021-03-11
## 252 Mar 10, 2021 6714.1 6,771.20 6,806.50 6,714.10 619.77M -0.84% 2021-03-10
## 253 Mar 09, 2021 6771.2 6,739.60 6,810.00 6,739.60 733.62M 0.47% 2021-03-09
## 254 Mar 08, 2021 6739.6 6,710.80 6,835.60 6,710.80 609.64M 0.43% 2021-03-08
## 255 Mar 05, 2021 6710.8 6,760.70 6,760.70 6,660.50 702.40M -0.74% 2021-03-05
## 256 Mar 04, 2021 6760.7 6,818.00 6,818.00 6,709.00 737.49M -0.84% 2021-03-04
## 257 Mar 03, 2021 6818.0 6,762.30 6,818.80 6,762.30 591.22M 0.82% 2021-03-03
## 258 Mar 02, 2021 6762.3 6,789.60 6,860.70 6,762.30 739.31M -0.40% 2021-03-02
## 259 Mar 01, 2021 6789.6 6,673.30 6,790.10 6,672.20 734.33M 1.74% 2021-03-01
## 260 Feb 26, 2021 6673.3 6,834.00 6,834.00 6,658.90 1.03B -2.35% 2021-02-26
## 261 Feb 25, 2021 6834.0 6,777.80 6,857.30 6,777.80 806.72M 0.83% 2021-02-25
## 262 Feb 24, 2021 6777.8 6,839.20 6,839.20 6,762.60 677.31M -0.90% 2021-02-24
## 263 Feb 23, 2021 6839.2 6,780.90 6,839.20 6,765.30 806.33M 0.86% 2021-02-23
## 264 Feb 22, 2021 6780.9 6,793.80 6,824.80 6,774.30 653.24M -0.19% 2021-02-22
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## 166 145 1 Australia 162759 140981 25788217
## 167 113 0 Australia 171692 113068 25788217
## 168 84 0 Australia 170025 126082 25788217
## 169 106 0 Australia 168921 102796 25788217
## 170 102 1 Australia 146118 130133 25788217
## 171 64 0 Australia 144830 98104 25788217
## 172 48 0 Australia 156750 99235 25788217
## 173 42 0 Australia 149630 82037 25788217
## 174 30 0 Australia 159464 93563 25788217
## 175 27 0 Australia 135044 116934 25788217
## 176 50 0 Australia 154817 162784 25788217
## 177 41 0 Australia 185318 155772 25788217
## 178 41 0 Australia 96951 150302 25788217
## 179 42 0 Australia 144885 126945 25788217
## 180 32 0 Australia 126034 100163 25788217
## 181 33 0 Australia 119973 91915 25788217
## 182 20 0 Australia 138434 93522 25788217
## 183 24 0 Australia 138922 96664 25788217
## 184 14 0 Australia 140892 59835 25788217
## 185 11 0 Australia 128643 60188 25788217
## 186 13 0 Australia 130810 75644 25788217
## 187 8 0 Australia 141336 70468 25788217
## 188 15 0 Australia 136010 47437 25788217
## 189 13 0 Australia 152075 42936 25788217
## 190 3 0 Australia 153338 67478 25788217
## 191 15 0 Australia 142808 73284 25788217
## 192 5 0 Australia 140885 54037 25788217
## 193 14 0 Australia 127564 89000 25788217
## 194 16 0 Australia 141245 89027 25788217
## 195 4 0 Australia 143659 96003 25788217
## 196 13 0 Australia 141259 93405 25788217
## 197 6 0 Australia 138705 68241 25788217
## 198 13 0 Australia 119139 76663 25788217
## 199 9 0 Australia 121610 95647 25788217
## 200 11 0 Australia 124871 81048 25788217
## 201 17 0 Australia 111388 61494 25788217
## 202 17 0 Australia 104658 35564 25788217
## 203 4 0 Australia 101146 55335 25788217
## 204 3 0 Australia 92874 58524 25788217
## 205 9 0 Australia 95530 39354 25788217
## 206 5 0 Australia 83187 36132 25788217
## 207 7 0 Australia 76153 54093 25788217
## 208 2 0 Australia 85874 55232 25788217
## 209 9 0 Australia 82284 61475 25788217
## 210 8 0 Australia 76379 35191 25788217
## 211 7 0 Australia 72886 36799 25788217
## 212 13 0 Australia 73194 48969 25788217
## 213 9 0 Australia 81002 49746 25788217
## 214 19 0 Australia 77215 53237 25788217
## 215 15 0 Australia 79345 30733 25788217
## 216 12 0 Australia 56354 28900 25788217
## 217 13 0 Australia 55300 44266 25788217
## 218 25 0 Australia 67259 50422 25788217
## 219 23 0 Australia 82741 59461 25788217
## 220 33 0 Australia 60207 42490 25788217
## 221 37 0 Australia 31852 40627 25788217
## 222 15 0 Australia 58446 33457 25788217
## 223 44 0 Australia 69903 37186 25788217
## 224 18 0 Australia 67591 37749 25788217
## 225 20 0 Australia 64821 27878 25788217
## 226 23 0 Australia 66730 28648 25788217
## 227 15 0 Australia 53981 39152 25788217
## 228 15 0 Australia 61272 44406 25788217
## 229 19 0 Australia 63633 45793 25788217
## 230 21 0 Australia 60991 28512 25788217
## 231 10 1 Australia 56379 32317 25788217
## 232 6 0 Australia 61355 50988 25788217
## 233 5 0 Australia 81297 44549 25788217
## 234 6 0 Australia 75880 32561 25788217
## 235 14 0 Australia 65351 28581 25788217
## 236 11 0 Australia 79283 85667 25788217
## 237 18 0 Australia 73979 84676 25788217
## 238 8 0 Australia 72826 44989 25788217
## 239 20 0 Australia 55764 45086 25788217
## 240 13 0 Australia 46310 38818 25788217
## 241 9 0 Australia 51745 39005 25788217
## 242 9 0 Australia 49908 47726 25788217
## 243 10 0 Australia 46000 29572 25788217
## 244 5 0 Australia 30542 30548 25788217
## 245 9 0 Australia 13077 44082 25788217
## 246 17 0 Australia 14697 50496 25788217
## 247 12 0 Australia 22500 54260 25788217
## 248 17 0 Australia 21120 33843 25788217
## 249 7 0 Australia 17656 32312 25788217
## 250 10 0 Australia 24185 45015 25788217
## 251 12 0 Australia 10109 51011 25788217
## 252 16 0 Australia 16672 47309 25788217
## 253 13 0 Australia 13420 27603 25788217
## 254 15 0 Australia 8539 35146 25788217
## 255 8 0 Australia 5073 47635 25788217
## 256 14 0 Australia 10858 49049 25788217
## 257 11 0 Australia 9939 52921 25788217
## 258 10 0 Australia 9163 39974 25788217
## 259 8 0 Australia 7277 24226 25788217
## 260 8 0 Australia 6490 47899 25788217
## 261 10 0 Australia 6881 49940 25788217
## 262 8 0 Australia 9715 56776 25788217
## 263 2 0 Australia 4125 43521 25788217
## 264 7 0 Australia 2769 36501 25788217
## population_density
## 1 3.202
## 2 3.202
## 3 3.202
## 4 3.202
## 5 3.202
## 6 3.202
## 7 3.202
## 8 3.202
## 9 3.202
## 10 3.202
## 11 3.202
## 12 3.202
## 13 3.202
## 14 3.202
## 15 3.202
## 16 3.202
## 17 3.202
## 18 3.202
## 19 3.202
## 20 3.202
## 21 3.202
## 22 3.202
## 23 3.202
## 24 3.202
## 25 3.202
## 26 3.202
## 27 3.202
## 28 3.202
## 29 3.202
## 30 3.202
## 31 3.202
## 32 3.202
## 33 3.202
## 34 3.202
## 35 3.202
## 36 3.202
## 37 3.202
## 38 3.202
## 39 3.202
## 40 3.202
## 41 3.202
## 42 3.202
## 43 3.202
## 44 3.202
## 45 3.202
## 46 3.202
## 47 3.202
## 48 3.202
## 49 3.202
## 50 3.202
## 51 3.202
## 52 3.202
## 53 3.202
## 54 3.202
## 55 3.202
## 56 3.202
## 57 3.202
## 58 3.202
## 59 3.202
## 60 3.202
## 61 3.202
## 62 3.202
## 63 3.202
## 64 3.202
## 65 3.202
## 66 3.202
## 67 3.202
## 68 3.202
## 69 3.202
## 70 3.202
## 71 3.202
## 72 3.202
## 73 3.202
## 74 3.202
## 75 3.202
## 76 3.202
## 77 3.202
## 78 3.202
## 79 3.202
## 80 3.202
## 81 3.202
## 82 3.202
## 83 3.202
## 84 3.202
## 85 3.202
## 86 3.202
## 87 3.202
## 88 3.202
## 89 3.202
## 90 3.202
## 91 3.202
## 92 3.202
## 93 3.202
## 94 3.202
## 95 3.202
## 96 3.202
## 97 3.202
## 98 3.202
## 99 3.202
## 100 3.202
## 101 3.202
## 102 3.202
## 103 3.202
## 104 3.202
## 105 3.202
## 106 3.202
## 107 3.202
## 108 3.202
## 109 3.202
## 110 3.202
## 111 3.202
## 112 3.202
## 113 3.202
## 114 3.202
## 115 3.202
## 116 3.202
## 117 3.202
## 118 3.202
## 119 3.202
## 120 3.202
## 121 3.202
## 122 3.202
## 123 3.202
## 124 3.202
## 125 3.202
## 126 3.202
## 127 3.202
## 128 3.202
## 129 3.202
## 130 3.202
## 131 3.202
## 132 3.202
## 133 3.202
## 134 3.202
## 135 3.202
## 136 3.202
## 137 3.202
## 138 3.202
## 139 3.202
## 140 3.202
## 141 3.202
## 142 3.202
## 143 3.202
## 144 3.202
## 145 3.202
## 146 3.202
## 147 3.202
## 148 3.202
## 149 3.202
## 150 3.202
## 151 3.202
## 152 3.202
## 153 3.202
## 154 3.202
## 155 3.202
## 156 3.202
## 157 3.202
## 158 3.202
## 159 3.202
## 160 3.202
## 161 3.202
## 162 3.202
## 163 3.202
## 164 3.202
## 165 3.202
## 166 3.202
## 167 3.202
## 168 3.202
## 169 3.202
## 170 3.202
## 171 3.202
## 172 3.202
## 173 3.202
## 174 3.202
## 175 3.202
## 176 3.202
## 177 3.202
## 178 3.202
## 179 3.202
## 180 3.202
## 181 3.202
## 182 3.202
## 183 3.202
## 184 3.202
## 185 3.202
## 186 3.202
## 187 3.202
## 188 3.202
## 189 3.202
## 190 3.202
## 191 3.202
## 192 3.202
## 193 3.202
## 194 3.202
## 195 3.202
## 196 3.202
## 197 3.202
## 198 3.202
## 199 3.202
## 200 3.202
## 201 3.202
## 202 3.202
## 203 3.202
## 204 3.202
## 205 3.202
## 206 3.202
## 207 3.202
## 208 3.202
## 209 3.202
## 210 3.202
## 211 3.202
## 212 3.202
## 213 3.202
## 214 3.202
## 215 3.202
## 216 3.202
## 217 3.202
## 218 3.202
## 219 3.202
## 220 3.202
## 221 3.202
## 222 3.202
## 223 3.202
## 224 3.202
## 225 3.202
## 226 3.202
## 227 3.202
## 228 3.202
## 229 3.202
## 230 3.202
## 231 3.202
## 232 3.202
## 233 3.202
## 234 3.202
## 235 3.202
## 236 3.202
## 237 3.202
## 238 3.202
## 239 3.202
## 240 3.202
## 241 3.202
## 242 3.202
## 243 3.202
## 244 3.202
## 245 3.202
## 246 3.202
## 247 3.202
## 248 3.202
## 249 3.202
## 250 3.202
## 251 3.202
## 252 3.202
## 253 3.202
## 254 3.202
## 255 3.202
## 256 3.202
## 257 3.202
## 258 3.202
## 259 3.202
## 260 3.202
## 261 3.202
## 262 3.202
## 263 3.202
## 264 3.202
covid_temp <- covid_clean
covid_temp$month <- strftime(covid_temp$date, "%m")
covid_temp$year <- strftime(covid_temp$date, "%Y")
covid_temp_new_case_aggregate <- aggregate(new_cases_smoothed~month+year,
covid_temp,
FUN = mean)
covid_temp_new_case_aggregate$month_year <- paste(covid_temp_new_case_aggregate$month, covid_temp_new_case_aggregate$year)ggplot(covid_temp_new_case_aggregate, aes(x= new_cases_smoothed, y= month_year)) +
geom_bar(stat = 'identity') + ggtitle("Average New Cases (Smoothed) per Month") +
ylab("month year") + xlab("average new cases")covid_temp = covid_clean %>% select(date,new_people_vaccinated_smoothed)
covid_temp$date <- as.Date(covid_temp$date, format = "%Y-%m-%d")
covid_temp_new_vaccinated_aggregate = covid_temp %>% mutate(month_year = as.character(format(date, "%m-%Y"))) %>%
group_by(month_year) %>%
summarise(date=date[1], number = mean(new_people_vaccinated_smoothed))
covid_temp_new_vaccinated_aggregate ggplot(covid_temp_new_vaccinated_aggregate, aes(x = number, y= month_year)) +
geom_bar(stat = 'identity') + ggtitle("Average New People Vaccinated (Smoothed) per Month") +
ylab("month year") + xlab("average new people vaccinated")#glimpse(df_aus)
df_aus_subset = df_aus %>% select(Price, new_cases)
view(df_aus_subset)
M0 = lm(Price ~ new_cases, data = df_aus_subset) # Null model
summary(M0)##
## Call:
## lm(formula = Price ~ new_cases, data = df_aus_subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -549.62 -157.03 60.97 163.28 405.41
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.223e+03 1.525e+01 473.504 <2e-16 ***
## new_cases 1.166e-03 5.762e-04 2.024 0.044 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 226.3 on 262 degrees of freedom
## Multiple R-squared: 0.0154, Adjusted R-squared: 0.01164
## F-statistic: 4.097 on 1 and 262 DF, p-value: 0.04396
Scatter Plot for price and new_cases
df_aus = df_aus[order(as.Date(df_aus$date, format="%d/%m/%Y")),]
df_aus$logPrice = log(df_aus$Price)
colnames(df_aus)[7] = 'Change'
#write.csv(df_aus,"df_aus.csv")y <- df_aus$Price
x <- df_aus$new_cases
plot(x, y, main = "Price ~ New Cases",
ylab = "Price", xlab = "new_cases",
pch = 19, frame = FALSE)
# Add regression line
plot(x, y, main = "Price ~ New Cases",
ylab = "Price", xlab = "new_cases",
pch = 19, frame = FALSE)
abline(lm(y ~ x, data = df_aus), col = "blue")# df_aus_subset = df_aus %>% select(Price, new_deaths) %>% filter(new_deaths != 0)df_aus_subset = df_aus %>% select(Price, new_deaths, new_vaccinations, new_cases)
view(df_aus_subset)
M1 = lm(Price ~ poly(new_vaccinations, degree=2), data=df_aus_subset)
summary(M1)##
## Call:
## lm(formula = Price ~ poly(new_vaccinations, degree = 2), data = df_aus_subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -555.39 -72.58 -0.10 77.58 697.87
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7235.494 8.446 856.70 <2e-16 ***
## poly(new_vaccinations, degree = 2)1 2467.685 137.228 17.98 <2e-16 ***
## poly(new_vaccinations, degree = 2)2 -1620.816 137.228 -11.81 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 137.2 on 261 degrees of freedom
## Multiple R-squared: 0.6394, Adjusted R-squared: 0.6367
## F-statistic: 231.4 on 2 and 261 DF, p-value: < 2.2e-16
ggplot(data = df_aus_subset, mapping = aes(x = Price, y = new_deaths)) + geom_point() + ggtitle("Prices of stocks and against new death") + ylab("New deaths") + xlab("Price")ggplot(data = df_aus, mapping = aes(x = new_vaccinations, y = Price)) + geom_point() + ggtitle("Prices of stocks against new vaccinations") + xlab("New vaccinations") + ylab("Price")df_aus_subset = df_aus %>% select(Price, new_tests)
view(df_aus_subset)
M2 = lm(Price ~ poly(new_tests, degree=2), data=df_aus_subset)
summary(M2)##
## Call:
## lm(formula = Price ~ poly(new_tests, degree = 2), data = df_aus_subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -469.40 -88.38 12.24 86.71 401.48
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7235.494 8.392 862.236 < 2e-16 ***
## poly(new_tests, degree = 2)1 2767.837 136.347 20.300 < 2e-16 ***
## poly(new_tests, degree = 2)2 -1057.592 136.347 -7.757 1.97e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 136.3 on 261 degrees of freedom
## Multiple R-squared: 0.6441, Adjusted R-squared: 0.6413
## F-statistic: 236.1 on 2 and 261 DF, p-value: < 2.2e-16
ggplot(data = df_aus, mapping = aes(x = new_tests, y = Price)) + geom_point() + ggtitle("Prices of stocks against new tests") + xlab("New tests") + ylab("Price")ggplot(data = df_aus, mapping = aes(x = Price, y = population)) + geom_point() + ggtitle("Prices of stocks against population") + ylab("New tests") + xlab("Price")ggplot(data = df_aus, mapping = aes(x = Price, y = population_density)) + geom_point() + ggtitle("Prices of stocks against population density") + ylab("New tests") + xlab("Price")ggplot(data = df_aus, mapping = aes(x = date, y = population_density)) + geom_line() + ggtitle("Prices of stocks against population density") + ylab("New tests") + xlab("Price") # badsummary(df_aus$Price)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6673 7067 7296 7235 7400 7629
df_aus$Price_mul30 = df_aus$Price*30
p1 = ggplot(data = df_aus) + geom_line(aes(x=date, y = new_cases), color = "red") + geom_line(aes(x=date, y = new_vaccinations), color = "light green") + geom_line(aes(x=date, y = new_tests), color = "light blue") +
geom_line(aes(x=date, y = Price_mul30), color = "blue") + theme_bw()
df_aus$Price_mul30 = df_aus$Price*30
p1 = ggplot(data = df_aus) + geom_line(aes(x=date, y = new_cases), color = "red") + geom_line(aes(x=date, y = new_vaccinations), color = "light green") + geom_line(aes(x=date, y = new_tests), color = "light blue")+ theme_bw()
ggplotly(p1)autoplot(M0, which = 1:2)
autoplot(M1, which = 1:2)
autoplot(M2, which = 1:2)M = lm(logPrice ~ polym(new_tests, new_vaccinations, degree=2, raw=TRUE), data=df_aus)
summary(M)##
## Call:
## lm(formula = logPrice ~ polym(new_tests, new_vaccinations, degree = 2,
## raw = TRUE), data = df_aus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.068522 -0.008047 0.001027 0.009477 0.044903
##
## Coefficients:
## Estimate
## (Intercept) 8.804e+00
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)1.0 3.844e-07
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)2.0 -3.683e-13
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)0.1 5.650e-07
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)1.1 -3.685e-13
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)0.2 -1.171e-12
## Std. Error
## (Intercept) 3.484e-03
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)1.0 5.923e-08
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)2.0 2.030e-13
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)0.1 4.766e-08
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)1.1 2.215e-13
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)0.2 1.302e-13
## t value Pr(>|t|)
## (Intercept) 2526.972 < 2e-16
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)1.0 6.491 4.34e-10
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)2.0 -1.814 0.0708
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)0.1 11.856 < 2e-16
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)1.1 -1.664 0.0974
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)0.2 -8.993 < 2e-16
##
## (Intercept) ***
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)1.0 ***
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)2.0 .
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)0.1 ***
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)1.1 .
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)0.2 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01542 on 258 degrees of freedom
## Multiple R-squared: 0.7704, Adjusted R-squared: 0.7659
## F-statistic: 173.1 on 5 and 258 DF, p-value: < 2.2e-16
ggplot(data = df_aus) + geom_point(aes(x = new_tests, y = logPrice), color = "light blue") +
geom_point(aes(x = new_vaccinations, y = logPrice), color = "light green") +
ggtitle("Prices of stocks against new tests and new_vaccinations") + ylab("Price") +theme_classic()